




"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				def __init__(			self						,					_a						,					_a						,					_a						,					_a = None						,					):





							"""simple docstring"""
							super().__init__()
							self.register_modules(transformer=_a						,					vae=_a						,					scheduler=_a					)

							# create a imagenet -> id dictionary for easier use
							lowerCamelCase      =   {}
							if idalabel is not None:
										for key, value in idalabel.items():
													for label in value.split(""","""					):
																lowerCamelCase      =   int(_a					)
										lowerCamelCase      =   dict(sorted(self.labels.items()					)					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""

							if not isinstance(_a						,					_a					):
										lowerCamelCase      =   list(_a					)

							for l in label:
										if l not in self.labels:
													raise ValueError(
													    f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'					)

							return [self.labels[l] for l in label]





				@torch.no_grad()
				def __call__(			self						,					_a						,					_a = 4.0						,					_a = None						,					_a = 50						,					_a = "pil"						,					_a = True						,					):





							"""simple docstring"""
							lowerCamelCase      =   len(_a					)
							lowerCamelCase      =   self.transformer.config.sample_size
							lowerCamelCase      =   self.transformer.config.in_channels

							lowerCamelCase      =   randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)						,					generator=_a						,					device=self.device						,					dtype=self.transformer.dtype						,					)
							lowerCamelCase      =   torch.cat([latents] * 2					) if guidance_scale > 1 else latents

							lowerCamelCase      =   torch.tensor(_a						,					device=self.device					).reshape(-1					)
							lowerCamelCase      =   torch.tensor([1_000] * batch_size						,					device=self.device					)
							lowerCamelCase      =   torch.cat([class_labels, class_null]						,					0					) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(_a					)

							for t in self.progress_bar(self.scheduler.timesteps					):
										if guidance_scale > 1:
													lowerCamelCase      =   latent_model_input[: len(_a					) // 2]
													lowerCamelCase      =   torch.cat([half, half]						,					dim=0					)
										lowerCamelCase      =   self.scheduler.scale_model_input(_a						,					_a					)

										lowerCamelCase      =   t
										if not torch.is_tensor(_a					):
													# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
													# This would be a good case for the `match` statement (Python 3.10+)
													lowerCamelCase      =   latent_model_input.device.type == """mps"""
													if isinstance(_a						,					_a					):
																lowerCamelCase      =   torch.floataa if is_mps else torch.floataa
													else:
																lowerCamelCase      =   torch.intaa if is_mps else torch.intaa
													lowerCamelCase      =   torch.tensor([timesteps]						,					dtype=_a						,					device=latent_model_input.device					)
										elif len(timesteps.shape					) == 0:
													lowerCamelCase      =   timesteps[None].to(latent_model_input.device					)
										# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
										lowerCamelCase      =   timesteps.expand(latent_model_input.shape[0]					)
										# predict noise model_output
										lowerCamelCase      =   self.transformer(
										    _a						,					timestep=_a						,					class_labels=_a					).sample

										# perform guidance
										if guidance_scale > 1:
													lowerCamelCase ,  lowerCamelCase      =   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
													lowerCamelCase ,  lowerCamelCase      =   torch.split(_a						,					len(_a					) // 2						,					dim=0					)

													lowerCamelCase      =   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
													lowerCamelCase      =   torch.cat([half_eps, half_eps]						,					dim=0					)

													lowerCamelCase      =   torch.cat([eps, rest]						,					dim=1					)

										# learned sigma
										if self.transformer.config.out_channels // 2 == latent_channels:
													lowerCamelCase ,  lowerCamelCase      =   torch.split(_a						,					_a						,					dim=1					)
										else:
													lowerCamelCase      =   noise_pred

										# compute previous image: x_t -> x_t-1
										lowerCamelCase      =   self.scheduler.step(_a						,					_a						,					_a					).prev_sample

							if guidance_scale > 1:
										lowerCamelCase ,  lowerCamelCase      =   latent_model_input.chunk(2						,					dim=0					)
							else:
										lowerCamelCase      =   latent_model_input

							lowerCamelCase      =   1 / self.vae.config.scaling_factor * latents
							lowerCamelCase      =   self.vae.decode(_a					).sample

							lowerCamelCase      =   (samples / 2 + 0.5).clamp(0						,					1					)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowerCamelCase      =   samples.cpu().permute(0						,					2						,					3						,					1					).float().numpy()

							if output_type == "pil":
										lowerCamelCase      =   self.numpy_to_pil(_a					)

							if not return_dict:
										return (samples,)

							return ImagePipelineOutput(images=_a					)









"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        __magic_name__				(			UpperCAmelCase__		,   unittest.TestCase							):






				'''simple docstring'''
				__UpperCamelCase         =		DiTPipeline
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
				__UpperCamelCase         =		PipelineTesterMixin.required_optional_params - {
				    "latents",
				    "num_images_per_prompt",
				    "callback",
				    "callback_steps",
				}
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
				__UpperCamelCase         =		False





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							torch.manual_seed(0					)
							lowerCamelCase      =   TransformeraDModel(
							    sample_size=16						,					num_layers=2						,					patch_size=4						,					attention_head_dim=8						,					num_attention_heads=2						,					in_channels=4						,					out_channels=8						,					attention_bias=_a						,					activation_fn="""gelu-approximate"""						,					num_embeds_ada_norm=1_000						,					norm_type="""ada_norm_zero"""						,					norm_elementwise_affine=_a						,					)
							lowerCamelCase      =   AutoencoderKL()
							lowerCamelCase      =   DDIMScheduler()
							lowerCamelCase      =   {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
							return components





				def   _lowerCAmelCase							(			self						,					_a						,					_a=0					):





							"""simple docstring"""
							if str(_a					).startswith("""mps"""					):
										lowerCamelCase      =   torch.manual_seed(_a					)
							else:
										lowerCamelCase      =   torch.Generator(device=_a					).manual_seed(_a					)
							lowerCamelCase      =   {
							    """class_labels""": [1],
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}
							return inputs





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   """cpu"""

							lowerCamelCase      =   self.get_dummy_components()
							lowerCamelCase      =   self.pipeline_class(**_a					)
							pipe.to(_a					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs(_a					)
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							self.assertEqual(image.shape						,					(1, 16, 16, 3)					)
							lowerCamelCase      =   np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]					)
							lowerCamelCase      =   np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(_a						,					1e-3					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_inference_batch_single_identical(relax_max_difference=_a						,					expected_max_diff=1e-3					)





				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()						,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,					)
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






@require_torch_gpu
@slow
class        __magic_name__				(			unittest.TestCase							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   torch.manual_seed(0					)

							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella""", """white shark""", """white wolf"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=40						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'					)
										assert np.abs((expected_image - image).max()					) < 1e-2





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""					)
							lowerCamelCase      =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   torch.manual_seed(0					)
							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=25						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    f'/dit/{word}_512.npy'					)

										assert np.abs((expected_image - image).max()					) < 1e-1



